Innovation in Retrieval-Augmented Generation: the ABBYY Approach

Among the most infamous challenges for businesses pursuing value through generative AI and large language models (LLMs) is their potential to generate inaccurate, irrelevant, or even harmful responses. As part of the ongoing trend towards AI specialization and customization for specified business needs, new tools and tactics have emerged to curtail these concerns of hallucination and other errant outputs.

One such development is retrieval augmented generation, or RAG, which has been a prominent pillar of recent tech headlines. ABBYY has contributed extensively to RAG discussions through both the ABBYY blog and as a sought-out expert in leading tech publications.

From defining and getting started with RAG to learning how agentic RAG stacks up to its “traditional” predecessor, following are highlights of recent media coverage that help business leaders understand this technology and how it applies to their AI initiatives.

Understanding RAG

The underlying key to successful use of generative AI is a foundation of high-quality data, as AI tools can only provide outputs as valuable as the data they learn from.

RAG involves retrieving information from specified external knowledge sources to imbue generative AI and LLMs with contextually relevant and accurate data, increasing the likelihood of a valuable and actionable outcome. As Max Vermeir told ZDNET in their article on integrating generative AI LLMs with business knowledge, “this combination allows the LLM to reason not just on its own pre-existing knowledge but also on the actual knowledge you provide through specific prompts. This process results in more accurate and contextually relevant answers.” ¹

If generative AI is a scholar and your company’s data is their vast library, RAG functions as the index to guide them to the information they need, preventing them from becoming lost, distracted, or confused on their journey to epiphany.

ZDNET continues to emphasize the importance of integrating deep knowledge of your business environment to ensure that an LLM is more than a generic tool, but rather “a specialized assistant that understands the nuances of your business, operations, products, and services.”

Agentic RAG: Moving the needle

RAG quickly caught on as an effective aid to generating value through AI, prompting rapid development of even more effective information retrieval techniques.

Namely, agentic RAG emerged as a method to further optimize LLMs through its ability to “deal with more complex queries and better ensure the accuracy of retrieved information,” according to Max's article in AiThority.² By deploying “intelligent agents” to verify data through multi-step reasoning and cross-referencing sources, agentic RAG enables greater precision and autonomy.

While its debut was hot on the heels of “traditional” RAG, the increased effectiveness of agentic RAG is marked enough to justify the use of such an adjective to describe its earlier counterpart. Still, both approaches bear a shared burden: data quality is key, and a knowledgebase polluted with poor-quality data will severely limit agentic RAG’s success. So, what are the most significant differentiators between the two?

As Max wrote in ITProToday, “the main difference between traditional RAG systems and agentic RAG is that the former relies on singular queries to generate responses.” ³ Thus, traditional RAG is often unable to adapt to new information, nor a businesses’ dynamic circumstances and evolving needs.

Furthermore, the capacity of agentic RAG for multifaceted queries and multimodal integration (such as incorporating data beyond text like images and audio) enables more comprehensive understanding and thus more relevant responses. Agentic RAG can also be used in tandem with search engines and APIs to enhance real-time gathering of data.

Preparing your organization for RAG

RAG’s dependence on data warrants preparation to ensure it has access to the valuable insights locked within your business data. This consists of digitizing documents, extracting their data, structuring it for AI consumption, and using an API to put it together.

Optical character recognition (OCR) solutions are helpful for this process, but leveraging intelligent document processing (IDP) solutions with advanced AI-powered OCR capabilities will enable superior accuracy and speed.

This is exemplified by the pre-trained AI skills in the ABBYY Marketplace, which enable the ABBYY Vantage platform for IDP to extract and understand data from documents with unparalleled speed and accuracy, regardless of language, format, type, or structure. As described in ABBYY CMO Bruce Orcutt’s comments published by Techstrong.ai, this streamlined conversion of company knowledge accelerates enterprises’ ability to leverage proprietary data with their models and “take the next step towards maturity in LLM use cases.”⁴

Stay updated on when ABBYY is featured in prominent tech media by visiting the ABBYY Newsroom, and find more in-depth explorations of current AI topics in the ABBYY blog and The Intelligent Enterprise.

Sources

1. ZDNET, “Understanding RAG: How to integrate generative AI LLMs with your business knowledge” (Jason Perlow, 2024)
2. AiThority, “Agentic RAG – the Path to more Accurate Data” (Maxime Vermeir, 2024)
3. ITProToday, “Agentic RAG vs. Traditional RAG: Which Improves AI Capabilities More?” (Maxime Vermeir, 2024)
4. Techstrong.ai, “ABBYY Mixes LLM Models and RAG Into Its IDP Marketplace” (Jeff Burt, 2024)